Retention is the most important factor to growth. Retention curves could be constructed for for even small amounts (100s) of data. X-axis indicates days from acquisition and the most important information should be shown after 28 or 30 days. Y-axis should be the key measurement that fits your product. Some examples include, activity, engagement, and revenue.

Three types of retention curves exist. For most products, you want to see your retention curve be a flat line parallel to the x-axis. For smash hits like games, you can easily expect the retention curve to actually hit the x-axis. This is true for games. Games smash hitters where very active users exists in the beginning and slowly dies down to zero. The third type of retention curve grows after slowing down.

Pink line shows daily average spending from users who signed up x days ago. As you can see, most users on first few days spend. The curve trails down and stabilize quite fast. With this data, we can easily predict the 200 days of average spending by looking at the 100 days data.

What causes retention to grow and stabilize?

Network effects: Users form a community within a product and don’t leave

New Interface: iOS vs Android, new users are coming to the product. Change in layout could also lure users back in.

Addition Categories: eBay and Amazon started off few categories and had an active base of users. Adding addition categories created more content for users to get involved and also brought in new users who are interested in new categories.

Better way to measure growth is not by new users alone. We also want to know who are dropping out (churning users), and also those who left for a bit but came back (resurrecting users).

New users are gained by identifying the “Magic Moment.” This is the first time a user decides to be part of the product. For social networks, this could be seeing your friends on the product. For online markets, this could be seeing the item you wish to purchase.